如何使用字符串访问Pandas DataFrame日期时间索引 [英] How to access pandas DataFrame datetime index using strings

问题描述

这是一个非常简单实用的问题.我觉得它必须是一个愚蠢的细节，并且应该有类似的问题.我找不到他们.如果有人这样做，我会很乐意将其删除.

我找到的最接近的是: 熊猫:使用loc遍历DataFrame索引

解决方案

pandas正在研究[]内部的内容并决定应该做什么.如果它是列名的子集，则将返回包含这些列的DataFrame.如果它是索引值的范围，它将返回这些行的子集.无法处理的是获取单个索引值.

解决方案

两个解决方法

1.将参数转换为pandas解释为范围的某种东西.

df['2008-01-01':'2008-01-01']

2.使用为您提供此结果的方法. loc[]

df.loc['2008-01-01']

链接到文档

This is a very simple and practical question. I have the feeling that it must be a silly detail and that there should be similar questions. I wasn't able to find them tho. If someone does I'll happily delete this one.

The closest I found were these: pandas: iterating over DataFrame index with loc

How to select rows within a pandas dataframe based on time only when index is date and time

anyway, the thing is, I have a datetime indexed panda dataframe as follows:

In[81]: y
Out[81]: 
            PETR4  CSNA3  VALE5
2008-01-01    0.0    0.0    0.0
2008-01-02    1.0    1.0    1.0
2008-01-03    7.0    7.0    7.0

In[82]: y.index
Out[82]: DatetimeIndex(['2008-01-01', '2008-01-02', '2008-01-03'], dtype='datetime64[ns]', freq=None)

Oddly enough, I can't access its values using none of the following methods:

In[83]: y[datetime.datetime(2008,1,1)]
In[84]: y['2008-1-1']
In[85]: y['1/1/2008']

I get the KeyError error.

Even more weird is that the following methods DO work:

In[86]: y['2008']
Out[86]: 
            PETR4  CSNA3  VALE5
2008-01-01    0.0    0.0    0.0
2008-01-02    1.0    1.0    1.0
2008-01-03    7.0    7.0    7.0
In[87]: y['2008-1']
Out[87]: 
            PETR4  CSNA3  VALE5
2008-01-01    0.0    0.0    0.0
2008-01-02    1.0    1.0    1.0
2008-01-03    7.0    7.0    7.0

I'm fairly new to pandas so maybe I'm missing something here?

解决方案

pandas is taking what's inside the [] and deciding what it should do. If it's a subset of column names, it'll return a DataFrame with those columns. If it's a range of index values, it'll return a subset of those rows. What is does not handle is taking a single index value.

Solution

Two work around's

1.Turn the argument into something pandas interprets as a range.

df['2008-01-01':'2008-01-01']

2.Use the method designed to give you this result. loc[]

df.loc['2008-01-01']

Link to the documentation

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